Using a multi variate pattern analysis (MVPA) approach to decode FMRI responses to fear and anxiety.
Date on Master's Thesis/Doctoral Dissertation
5-2017
Document Type
Master's Thesis
Degree Name
M.S.
Department
Computer Engineering and Computer Science
Degree Program
Computer Science, MS
Committee Chair
Yampolskiy, Roman
Committee Co-Chair (if applicable)
Depue, Brendan
Committee Member
Imam, Ibrahim
Author's Keywords
neuroimaging; machine learning
Abstract
This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.
Recommended Citation
Torabian Esfahani, Sajjad, "Using a multi variate pattern analysis (MVPA) approach to decode FMRI responses to fear and anxiety." (2017). Electronic Theses and Dissertations. Paper 2652.
https://doi.org/10.18297/etd/2652
